notes.md

About Composing Programs

• how computers interpret computer programs and carry out computational processes

• some recent progress in computer programs

• 现在的编程工作更多是调用和组合别人写的轮子，而不像以前那样需要对轮子如何抽象和实现理解得很清楚

ch2 - Data Abstraction - Building Abstractions by combining data objects to form compound data

• data abstraction manipulate compound values as units

  • how data are manipulated (as units)

  • how data are represented (as parts)

    • message passing - how to form compound data using only procedures

      • manipulate procedures as objects automatically provides the ability to represent compound data

    • paris in scheme

      • closure property -> create hierarchical structures
        • the elements of a combination can themselves be combinations

    • sequences

      • list, tree

  • the interface between these two parts of system will be a set of functions, called selectors and constructors

    • like in data_abstraction.py, function numer() and denom() as selectors, and function rational() as constructors to construct the function add_rational(), mul_rational()

• Sequence paradigm (implemented as lists) as conventional interface

  • signal-flow structure

    • express programs as sequence operations,and make program designs that are modular
    • modular design by providing a library of components together with a conventional interface for connecting the components in flexible ways

  • chaining together a pipeline of functions, if the functions all take a sequence as an input and output

    • like sequences.py

• OOP

  • like oop.py

• Object Abstraction

  • like generic_function.py
  • generic functions are methods or functions that apply to argumetns of diff types
    • generic_function_1.py

• mutable Data

  • how data can be endowed with behavior using message passing and an object system

  • non-local assignment give the ability to maintain some state that is local to a function, but evolves over successive calls to that function

  • Iterator

    • process elements of a container value sequentially
    • two components: next and a mechanism for signaling the end

  • in the example mutable_data.py, a mutable list is implemented, and the message passing fucntion is used.

ch1 - Building Abstractions with Functions

• represent procedures (descriptions of processes) as data

  • Lisp’s flexibility in handling procedures as data
  • blur the traditional distinction between “passive” data and “active” processes.

• The programming language serves a framework where we organize our ideas about computational processes

  • combine simple ideas to form more complex ideas

    • primitive expressions and statements
    • combination -> compound elements are built from simpler ones
    • abstraction -> compound elements can be named and manipulated as units

  • combine and abstract both data and functions

  • pure function

    • has no effects beyond returning a value
    • return the same value when called twice with the same args

  • Testing

    • assert

  • control abstraction using recursion, higher-order functions, generators, and streams

    • higher-order functions
      • how functions can be manipulated as data using higher-order functions

ch4 - Data Processing

• lazy computation - computing values on demand instead of being retrieved from an existing memory source
  • TODO - Implement stream class

ch3 - language abstraction using interpreters and macros